Maintaining machinery can be expensive and logistically challenging. Often, complex machines with many different interrelated components, such as vehicles, are maintained based on a maintenance schedule that is time-based, or are reactively maintained in response to actual failures of the machines. Because time-based maintenance typically does not take into consideration the conditions under which the machine is operating, maintenance may be performed prior to or subsequent to an actual need for maintenance. In the context of a relatively large fleet of machines, replacing parts that do not need replacement can be expensive and unnecessarily removes the machine from operation during the unnecessary maintenance. Collectively, such time-based maintenance can make it difficult or impossible to optimize mission availability of a fleet of machines.
There is increased interest in health management systems, such as condition-based maintenance (CBM) systems and prognostics and health management (PHM) systems that attempt to more closely align maintenance of a machine with an actual need for maintenance. In such systems, sensor information may be collected from strategically placed sensors located on the machine, and periodically offloaded to a remote device, such as a server, which can then perform data mining and other analysis to generate diagnostic and prognostic reports on the machine based on the sensor information.
Periodic analysis of data for purposes of diagnostics and prognostics, while an improvement over time-based maintenance, may not diagnose in a timely manner a problem that has only just arisen. The intervals between periods of analysis may result in maintenance issues being undiagnosed in a timely manner. Where timely diagnostics and prognostics of the complex machine can be the difference between a safe and an unsafe operation, such as machines used in the military, or machines such as airplanes and helicopters, periodic analysis of data may be insufficient.
Accordingly, there is a need for real-time health management mechanisms that can generate diagnostic and prognostic results based on real-time sensor information, and that can, over time, improve the diagnostic and prognostic results based on historic knowledge of the respective machine.